Abstract
Systems and methods can automate the analysis of cloud service consumption to assist with challenges in selecting between a-la-carte and bundled purchasing models, as manual evaluation may be slow and resource-intensive. A computational framework, which can operate on a computing device such as a server or personal computer, may leverage a large language model to ingest and analyze diverse data sources, for example, billing information, system logs, and service documentation. The system can simulate the financial and operational impact of transitioning to a bundled service, using semantic reasoning to assess functional equivalence beyond simple keyword matching. This automated evaluation process can provide data driven recommendations to assist in the selection of cost effective cloud architectures while considering functional requirements.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mamgain, Paras; Saravanan, Sridharshini; and Singh, Jaspreet, "Automated Cloud Service Optimization via Simulated Transitions and Feature Parity Analysis", Technical Disclosure Commons, (July 07, 2026)
https://www.tdcommons.org/dpubs_series/10823